{
  "data": {
    "a": {
      "slug": "hugging-face-inference-providers",
      "name": "Hugging Face Inference Providers",
      "vendor": "Hugging Face, Inc.",
      "vendorUrl": "https://huggingface.co",
      "kind": "router",
      "category": "inference",
      "summary": "Hugging Face's serverless router that sends model calls to partner inference providers such as Groq, Cerebras, Together and Fireworks through one Hugging Face token, with an OpenAI-compatible API at router.huggingface.co/v1 and Python and JavaScript clients.",
      "url": "https://www.anchorterminal.com/tools/hugging-face-inference-providers",
      "markdownUrl": "https://www.anchorterminal.com/tools/hugging-face-inference-providers.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/hugging-face-inference-providers.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/hugging-face-inference-providers.json",
      "dns": "hugging-face-inference-providers.grade.anchorterminal.com",
      "repo": "https://github.com/huggingface/huggingface_hub",
      "license": "Proprietary service under the Hugging Face Terms of Service, which name Inference Providers. The `huggingface_hub` Python client is Apache-2.0 and `@huggingface/inference` is MIT",
      "transports": [
        "http"
      ],
      "remoteUrl": "https://router.huggingface.co/v1",
      "packages": [
        {
          "registry": "pypi",
          "name": "huggingface_hub"
        },
        {
          "registry": "npm",
          "name": "@huggingface/inference"
        }
      ],
      "auth": "api-key",
      "authNotes": "Hugging Face user access token sent as `Authorization: Bearer $HF_TOKEN`. The docs recommend a fine-grained token with only the `Make calls to Inference Providers` permission. Tokens are made in account settings in a browser, can be rotated and revoked, and a leaked token can be revoked by anyone through `POST /api/credentials/revoke`. A provider's own key can be stored in settings instead, and Hugging Face swaps it in when routing.",
      "pricing": "usage",
      "pricingNotes": "Pay as you go at each provider's own per-token rates with no Hugging Face markup, drawn from prepaid credits on the Hugging Face account. Free users get no included credits since 7 October 2026 and must buy credits. PRO users and Team or Enterprise seats get $2.00 a month of compute credits. Requests with a custom provider key are billed by the provider (https://huggingface.co/docs/inference-providers/pricing, checked 2026-11-06).",
      "priceSummary": "$2 / mo",
      "where": "hosted",
      "x402": {
        "level": "no",
        "evidence": "No x402, MPP or L402 in the Inference Providers docs, llms.txt or the router's model list. Calls draw on prepaid Hugging Face credits bought in the account's billing settings (checked 2026-11-06).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": 483470,
        "pypiWeekly": 71518708,
        "asOf": "2026-11-06"
      },
      "docsUrl": "https://huggingface.co/docs/inference-providers/index",
      "llmsTxt": "https://huggingface.co/docs/inference-providers/llms.txt",
      "capabilities": [
        "inference.router",
        "inference.open-weights",
        "inference.llm"
      ],
      "tags": [
        "hosted",
        "router",
        "usage",
        "prepaid",
        "openai-compatible",
        "llms-txt",
        "python",
        "javascript",
        "status-page",
        "soc2"
      ],
      "lastRelease": "2026-10-29",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 54,
        "grade": "C",
        "agentReady": false,
        "rank": 928,
        "ranked": true,
        "rankOf": 1515,
        "categoryRank": 28,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "ergonomics": 58,
          "maintenance": 80,
          "payments": 20,
          "reliability": 25,
          "schema": 62,
          "security": 79,
          "transparency": 71
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "medium",
          "date": "2026-11-06"
        },
        "negative": 0,
        "verdict": "One Hugging Face token reaches open models on 18 partner providers at the providers' own prices, with a public model list that shows each provider's price, context and tool support. Router rate limits, error responses and its own status component were not found in the reviewed documentation, and free accounts have had no included credits since 7 October 2026.",
        "bestFor": "Agents and developers who want open-weight models from several hosts on one token and one bill, with a public price and capability list per provider.",
        "strengths": [
          "router.huggingface.co/v1/models lists every routed model with each provider's price, context, tool support, latency and throughput, without a token",
          "Provider choice by suffix (`:fastest`, `:cheapest`, `:preferred` or a provider name) on an OpenAI-compatible endpoint",
          "Fine-grained tokens can be limited to the `Make calls to Inference Providers` permission and revoked at once",
          "No markup on provider rates, and request bodies and responses are not stored when routed",
          "Official clients in Python (`huggingface_hub` 2.4.0, 29 October 2026) and JavaScript (`@huggingface/inference` 4.13.32)"
        ],
        "weaknesses": [
          "No rate limits, 429 behaviour or error responses documented for the router",
          "status.huggingface.co has no component for Inference Providers or the router",
          "Free users have had no included credits since 7 October 2026, and buying credits needs a person in the account settings",
          "No OpenAPI document for the router, no batch API, and the Responses API is beta",
          "Data handling past the router depends on each provider's own policy"
        ],
        "agentNotes": [
          "Read https://router.huggingface.co/v1/models before choosing a provider; it shows price, context and `supports_tools` per provider",
          "Pin a provider with `model:\u003cprovider\u003e` when tool calling or structured output matters, since support varies by provider",
          "Use a fine-grained token with only the Inference Providers permission, and send `X-HF-Bill-To` to bill an organisation",
          "Back off on 429 without assuming a Retry-After header, since none is documented for the router"
        ],
        "metrics": {
          "kind": "remote",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "medium",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 54
          }
        ],
        "editorialScores": {
          "ergonomics": 58,
          "maintenance": 80,
          "payments": 20,
          "reliability": 25,
          "schema": 62,
          "security": 79,
          "transparency": 58
        },
        "provenanceScore": 84,
        "checked": {
          "categories": {
            "ergonomics": "2026-11-06",
            "maintenance": "2026-11-06",
            "payments": "2026-11-06",
            "reliability": "2026-11-06",
            "schema": "2026-11-06",
            "security": "2026-11-06",
            "transparency": "2026-11-06"
          },
          "last": "2026-11-06",
          "oldest": "2026-11-06",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "pip install huggingface_hub   # or: pip install openai, with base_url https://router.huggingface.co/v1",
        "http": "curl https://router.huggingface.co/v1/chat/completions \\\n  -H \"Authorization: Bearer $HF_TOKEN\" -H \"Content-Type: application/json\" \\\n  -d '{\"model\":\"openai/gpt-oss-120b:fastest\",\"messages\":[{\"role\":\"user\",\"content\":\"How many Gs in huggingface?\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/inference.router",
        "tool": "https://letme.dev/hugging-face-inference-providers"
      },
      "sameCompany": [
        "hugging-face-inference-endpoints",
        "smolagents",
        "carbon-x",
        "smollm3-3b"
      ],
      "area": "models",
      "unitPrices": [
        {
          "item": "openai/gpt-oss-120b input, cheapest provider listed (DeepInfra)",
          "unit": "1m-tokens",
          "usd": 0.037,
          "note": "Output $0.17. Groq lists $0.15/$0.75 and Cerebras $0.35/$0.75, per the router's model list of 6 November 2026"
        },
        {
          "item": "PRO monthly compute credits",
          "unit": "month",
          "usd": 2,
          "note": "Also $2.00 per seat for Team and Enterprise organisations"
        }
      ],
      "provenance": {
        "legalEntity": "Hugging Face, Inc.",
        "domain": "huggingface.co",
        "domainRegistered": "",
        "endpointOnVendorDomain": true,
        "terms": "https://huggingface.co/terms-of-service",
        "privacy": "https://huggingface.co/privacy",
        "statusPage": "https://status.huggingface.co",
        "changelog": "https://huggingface.co/changelog",
        "securityTxt": "valid",
        "checked": "2026-11-06",
        "notes": [
          "The Terms of Service (effective 15 September 2022) name Hugging Face, Inc. and define Inference Providers as a hosted service for running inference through an API on Hub models.",
          "huggingface.co/robots.txt and router.huggingface.co/robots.txt allow all. status.huggingface.co serves an empty robots.txt.",
          "status.huggingface.co is a Better Stack page with components for the Hub, git hosting, Inference Endpoints, Spaces, the CDNs and Jobs, and none for Inference Providers or the router.",
          "huggingface.co/changelog covers the whole Hub and had no Inference Providers entry on its first page on 6 November 2026. Dated changes to the docs are in the hub-docs repository's history.",
          "security.txt, privacy policy and subprocessor list as recorded on the Inference Endpoints listing (checked 8 October and 2 November 2026); not re-read for this listing."
        ],
        "score": 84
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/hugging-face-inference-providers.json",
      "live": {
        "slug": "hugging-face-inference-providers",
        "probe": {
          "target": "https://router.huggingface.co/v1",
          "method": "get",
          "lastAt": "2026-11-09T20:49:55.213319157Z",
          "lastOk": true,
          "lastStatus": 404,
          "lastMs": 160,
          "authRequired": false,
          "uptime24h": 100,
          "uptime30d": 100,
          "p50ms24h": 109,
          "p95ms24h": 182,
          "samples24h": 244,
          "samples30d": 711,
          "days": [
            {
              "date": "2026-11-06",
              "probes": 13,
              "ok": 13
            },
            {
              "date": "2026-11-07",
              "probes": 243,
              "ok": 243
            },
            {
              "date": "2026-11-08",
              "probes": 243,
              "ok": 243
            },
            {
              "date": "2026-11-09",
              "probes": 212,
              "ok": 212
            }
          ],
          "since": "2026-11-06T00:00:00Z"
        },
        "vendorStatus": {
          "page": "https://status.huggingface.co",
          "indicator": "unknown",
          "summary": "no machine-readable status found",
          "checkedAt": "2026-11-09T20:23:52.687732822Z"
        },
        "versions": [
          {
            "registry": "github",
            "name": "huggingface/huggingface_hub",
            "version": "v2.4.0",
            "released": "2026-10-29",
            "seenAt": "2026-11-09T02:26:58.318764513Z"
          },
          {
            "registry": "npm",
            "name": "@huggingface/inference",
            "version": "4.13.32",
            "seenAt": "2026-11-09T02:26:57.547796305Z"
          },
          {
            "registry": "pypi",
            "name": "huggingface_hub",
            "version": "2.4.0",
            "released": "2026-10-29",
            "seenAt": "2026-11-09T02:26:57.41539201Z"
          }
        ],
        "githubStars": 4035,
        "npmWeekly": 469922,
        "pypiWeekly": 69521123,
        "securityTxt": {
          "url": "https://huggingface.co/.well-known/security.txt",
          "state": "valid",
          "expires": "2030-07-01T08:42:00.000Z",
          "checkedAt": "2026-11-09T16:06:16.073224257Z"
        },
        "llmsTxt": {
          "url": "https://huggingface.co/docs/inference-providers/llms.txt",
          "ok": true,
          "status": 200,
          "checkedAt": "2026-11-09T15:39:48.172644766Z"
        },
        "domain": {
          "domain": "huggingface.co",
          "checkedAt": "2026-11-08T14:53:11.106908704Z"
        },
        "pages": [
          {
            "url": "https://huggingface.co/docs/inference-providers/pricing",
            "kind": "pricing",
            "status": 200,
            "checkedAt": "2026-11-09T15:42:45.001667783Z",
            "changedAt": "0001-01-01T00:00:00Z",
            "fingerprint": "2cbe821ca9d2"
          }
        ],
        "github": {
          "repo": "huggingface/huggingface_hub",
          "checkedAt": "2026-11-09T09:47:40.883745167Z",
          "stars": 4035,
          "archived": false,
          "issuesEnabled": true,
          "pushedAt": "2026-11-06T03:01:39Z",
          "openIssues": 113,
          "openPRs": 42,
          "issuesOpened90d": 173,
          "issuesClosed90d": 229,
          "prsMerged90d": 269,
          "ci": {
            "branch": "main",
            "runs": 50,
            "success": 44,
            "failure": 6,
            "other": 0,
            "latest": "success",
            "latestAt": "2026-11-05T15:51:59Z"
          },
          "replies": {
            "sampled": 25,
            "unanswered": 5,
            "medianDays": 2.5
          }
        },
        "updatedAt": "2026-11-09T20:49:55.213319157Z"
      },
      "streak": {
        "days": 3,
        "from": "2026-11-06",
        "sinceWatching": true,
        "best": 3
      }
    },
    "answer": "Hugging Face Inference Providers scores 54 (C) on agent readiness against Muse Ember's 51.8 (D), and leads in 4 of 7 scored categories. Muse Ember leads on reliability and agent ergonomics.",
    "b": {
      "slug": "muse-ember",
      "name": "Muse Ember",
      "vendor": "Meta",
      "vendorUrl": "https://dev.meta.ai",
      "kind": "model",
      "category": "inference",
      "summary": "Muse Ember is Meta's smallest and most efficient hosted model, built for sub-agent tasks, tool calls, routing and work over large collections of files and images, taking text, images, video, audio and PDF with a 1M-token context.",
      "url": "https://www.anchorterminal.com/tools/muse-ember",
      "markdownUrl": "https://www.anchorterminal.com/tools/muse-ember.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/muse-ember.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/muse-ember.json",
      "dns": "muse-ember.grade.anchorterminal.com",
      "license": "Closed model, used through Meta Model API and Muse Code under the Meta Model API Terms of Service (last updated 9 October 2026). No open weights; Meta's open-weight models are Muse Spark Open and Muse Glimmer",
      "transports": [],
      "remoteUrl": "https://api.meta.ai/v1",
      "packages": [],
      "auth": "api-key",
      "authNotes": "A bearer key from the API keys tab of the Model API dashboard, read from MODEL_API_KEY in Meta's examples, after a browser sign-up with email, mobile number, Facebook, Google or GitHub. Keys belong to a team and share its limits, with no scopes found, per the meta-model-api listing.",
      "pricing": "usage",
      "pricingNotes": "$0.10 per million input tokens, $0.004 cached and $0.50 output for requests up to 200K input tokens, and $0.30, $0.012 and $1.50 for the whole request above that. Standard mode only, no Contributor discount. Web search grounding $2.50 per 1,000 queries. Included in every Muse Code plan. No free API allowance found (checked 2026-11-09).",
      "priceSummary": "Pay per use",
      "where": "hosted",
      "x402": {
        "level": "no",
        "evidence": "No x402, MPP or L402 on Meta's pricing page, which bills tokens to a team account with a card (checked 2026-11-09).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-09"
      },
      "docsUrl": "https://dev.meta.ai/docs/muse-ember",
      "llmsTxt": "https://dev.meta.ai/llms.txt",
      "capabilities": [
        "inference.llm"
      ],
      "tags": [
        "official",
        "hosted",
        "model",
        "multimodal",
        "vision",
        "reasoning",
        "long-context",
        "tool-calling",
        "openai-compatible",
        "anthropic-compatible",
        "usage-priced",
        "card-required",
        "closed-source",
        "llms-txt",
        "new"
      ],
      "lastRelease": "2026-11-09",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 51.8,
        "grade": "D",
        "agentReady": false,
        "rank": 1017,
        "ranked": true,
        "rankOf": 1515,
        "categoryRank": 36,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "ergonomics": 72,
          "maintenance": 46,
          "payments": 20,
          "reliability": 40,
          "schema": 60,
          "security": 64,
          "transparency": 53
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "medium",
          "date": "2026-11-09"
        },
        "negative": 0,
        "verdict": "A cheap, capable model for the repeated parts of agent work, at $0.10 and $0.50 per million tokens with a 1M context and Meta's figures putting it level with Claude Haiku 5.5 on fewer tokens. Launched today on an API with no readable status page, SLA or deprecation policy, and closed, with no weights.",
        "bestFor": "Agents that hand out many small, bounded tasks (sub-agents, tool loops, routing, extraction over thousands of files) and want a cheap multimodal model with a 1M context behind the same API as a bigger Muse model.",
        "strengths": [
          "$0.10 input, $0.004 cached and $0.50 output per million tokens up to 200K input, published without a login",
          "Text, image, video, audio and PDF in, a 1,048,576-token context and up to 524,288 output tokens",
          "The same request body as Muse Spark and Muse Wonder on Responses, Chat Completions and Messages endpoints",
          "Tool calling, structured output, computer use and search grounding",
          "Reasoning effort from minimal to max, with clear guidance on what to hand it and what not to",
          "3,000 requests and 4,000,000 tokens a minute per team, published"
        ],
        "weaknesses": [
          "No weights and no parameter count published, so it can't be run locally",
          "Requests over 200K input tokens are billed at three times the rate for the whole request",
          "Default reasoning effort is max, which spends output tokens on simple routing calls unless set lower",
          "No readable status page, SLA, deprecation policy or API changelog, per the meta-model-api listing",
          "Benchmark and cost figures are Meta's own, several on internal benchmarks"
        ],
        "agentNotes": [
          "Call https://api.meta.ai/v1/responses (or /chat/completions or /messages) with model muse-ember-1.0 and a bearer MODEL_API_KEY",
          "Set the effort explicitly, reasoning.effort on Responses or reasoning_effort on Chat Completions. It defaults to max, and none isn't accepted; minimal or low suits classification and routing",
          "Keep input under 200K tokens a request, cached tokens and previous_response_id history included, or the whole request bills at the long-context rate",
          "Use it for sub-agents and bounded repeated tasks, and a larger Muse model for the plan, as Meta advises",
          "Use strict structured output for extraction and routing steps",
          "In Claude Code, map the haiku alias and sub-agent model to muse-ember-1.0, per Meta's guide"
        ],
        "metrics": {
          "kind": "remote",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "medium",
            "grade": "D",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 51.8
          }
        ],
        "editorialScores": {
          "ergonomics": 72,
          "maintenance": 46,
          "payments": 20,
          "reliability": 40,
          "schema": 60,
          "security": 64,
          "transparency": 41
        },
        "provenanceScore": 64,
        "checked": {
          "categories": {
            "ergonomics": "2026-11-09",
            "maintenance": "2026-11-09",
            "payments": "2026-11-09",
            "reliability": "2026-11-09",
            "schema": "2026-11-09",
            "security": "2026-11-09",
            "transparency": "2026-11-09"
          },
          "last": "2026-11-09",
          "oldest": "2026-11-09",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "http": "curl -X POST \"https://api.meta.ai/v1/responses\" \\\n  -H \"Authorization: Bearer $MODEL_API_KEY\" \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n  \"model\": \"muse-ember-1.0\",\n  \"input\": \"Classify this support ticket as billing, bug, or feature request.\",\n  \"reasoning\": {\"effort\": \"low\"}\n}'"
      },
      "letme": {
        "capability": "https://letme.dev/inference.llm",
        "tool": "https://letme.dev/muse-ember"
      },
      "sameCompany": [
        "muse-spark-open",
        "muse-code",
        "muse-glimmer-30b"
      ],
      "sameModel": [
        "meta-model-api"
      ],
      "area": "models",
      "unitPrices": [
        {
          "item": "Muse Ember input",
          "unit": "1m-tokens",
          "usd": 0.1,
          "note": "Up to 200K input tokens a request; $0.30 above, for the whole request"
        },
        {
          "item": "Muse Ember cached input",
          "unit": "1m-tokens",
          "usd": 0.004,
          "note": "$0.012 above 200K input tokens"
        },
        {
          "item": "Muse Ember output",
          "unit": "1m-tokens",
          "usd": 0.5,
          "note": "$1.50 above 200K input tokens"
        }
      ],
      "provenance": {
        "legalEntity": "Meta Platforms, Inc.",
        "domain": "meta.ai",
        "domainRegistered": "2017-12-16",
        "endpointOnVendorDomain": true,
        "terms": "https://dev.meta.ai/legal/terms-of-service",
        "privacy": "https://www.facebook.com/privacy/policy/",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-11-09",
        "notes": [
          "Company and platform facts are the meta-model-api listing's (checked 4 November 2026): Meta Platforms, Inc., or Meta Platforms Ireland Limited in the European Region, under the 9 October 2026 terms; no readable status page; no Model API changelog; no security.txt on dev.meta.ai.",
          "dev.meta.ai's and research.meta.ai's robots.txt allow our reader on the pages read on 9 November. domainRegistered is carried from the meta-model-api listing's RDAP reading."
        ],
        "score": 64
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/muse-ember.json",
      "live": {
        "slug": "muse-ember",
        "probe": {
          "target": "https://api.meta.ai/v1",
          "method": "get",
          "lastAt": "2026-11-09T20:50:02.257490788Z",
          "lastOk": true,
          "lastStatus": 404,
          "lastMs": 46,
          "authRequired": false,
          "uptime24h": 100,
          "uptime30d": 100,
          "p50ms24h": 48,
          "p95ms24h": 72,
          "samples24h": 10,
          "samples30d": 10,
          "days": [
            {
              "date": "2026-11-09",
              "probes": 10,
              "ok": 10
            }
          ],
          "since": "2026-11-09T19:55:51.172718582Z"
        },
        "updatedAt": "2026-11-09T20:50:02.257490788Z"
      },
      "streak": {
        "days": 0,
        "from": "2026-11-09",
        "sinceWatching": true,
        "best": 0
      }
    },
    "edges": [
      {
        "by": 34,
        "category": "maintenance",
        "leads": "hugging-face-inference-providers",
        "name": "Maintenance \u0026 community",
        "reasons": {
          "a": "Model reading. huggingface_hub 2.4.0 tagged on 29 October 2026 and the Inference Providers docs updated daily by an automated job, last on 6 November (30). huggingface_hub 2.2.0, 2.3.0 and 2.4.0 and @huggingface/inference 4.13.28 to 4.13.32 in the 90 days (20). No dedicated changelog; the Hub has a forum and Discord, and GitHub issue replies were not read (8 of 15). Current official SDKs in Python and JavaScript (15). The Python client supports Python 3.10 and later, per the Inference Endpoints check; CI not read (7 of 10).",
          "b": "Model reading. Released 9 November 2026 (30). No deprecation policy or notice period found, per the platform reading (0 of 12). Dated launches on the API, the latest Muse Wonder on 12 October and Muse Ember on 9 November (8 of 8). No Model API changelog; a help centre with tickets (8 of 15). No SDK of Meta's own (0 of 15, 0 of 10). 30 + 0 + 8 + 8 + 0 = 46."
        },
        "scores": {
          "a": 80,
          "b": 46
        }
      },
      {
        "by": 18,
        "category": "transparency",
        "leads": "hugging-face-inference-providers",
        "name": "Transparency \u0026 trust",
        "reasons": {
          "a": "Closed service under Terms of Service that name Inference Providers, with an Apache-2.0 Python client and an MIT JavaScript client (20). The security page and the docs agree on no training, no stored bodies and 30-day logs, but the privacy policy dates from 28 March 2023 and each provider's own data policy governs the rest (18 of 30). No deprecation policy found. Provider support changes and the 7 October removal of free credits are dated only in the docs repository history (5 of 20). The privacy policy lists 11 subprocessors with countries per the Inference Endpoints check, and the docs name every provider a request can reach, though not where each runs (15 of 20).",
          "b": "Editorial half. Closed model under the Model API terms, with clear terms and law; no weights and no parameter count, unlike Muse Spark Open and Muse Glimmer (15). Standard content isn't used for training, retention periods not stated outside zero retention (18 of 30). No deprecation policy or dated notices (0). A subprocessor list linked but not read, no data locations (8 of 20). 15 + 18 + 0 + 8 = 41."
        },
        "scores": {
          "a": 71,
          "b": 53
        }
      },
      {
        "by": 15,
        "category": "reliability",
        "leads": "muse-ember",
        "name": "Reliability",
        "reasons": {
          "a": "Hosted lines. status.huggingface.co is a Better Stack page with 90-day component history for the Hub, Inference Endpoints, Spaces, the CDNs and Jobs, but no component for Inference Providers or the router (10 of 20). With no component, there is no readable incident history for the surface agents call, and the feed's 23 items from 10 June to 7 October 2026 don't name it (5). No rate limits for the router were found; the Hub rate-limits page gives numbers for Hub APIs, resolvers and pages only (0). No 429 or overload guidance for the router was found (0). No SLA found (0). Chat completions on the router is generally available; the Responses API is labelled beta (10). Routing between providers with `:fastest` or `:cheapest` softens a single provider's outage, which this checklist doesn't score.",
          "b": "Model reading, for the surface it's called on, Meta Model API. No status page with readable history, per the meta-model-api listing's 4 November check (0), so no readable incident history (5). Rate limits published per team and per model, 3,000 RPM and 4,000,000 TPM for muse-ember-1.0, read 9 November (15). 429 and 503 with Retry-After, backoff guidance and rate-limit headers, per the meta-model-api listing (15). No SLA found (0). Muse Ember carries no preview label, but the API opened as a public preview in July and no general-availability notice was found, so half, as for the platform (5). 0 + 5 + 15 + 15 + 0 + 5 = 40."
        },
        "scores": {
          "a": 25,
          "b": 40
        }
      },
      {
        "by": 15,
        "category": "security",
        "leads": "hugging-face-inference-providers",
        "name": "Security \u0026 auth",
        "reasons": {
          "a": "Model reading. Fine-grained user access tokens that can be limited to the `Make calls to Inference Providers` permission, rotated and revoked, plus a public revocation endpoint for leaked tokens, and organisation approval of fine-grained tokens on paid plans (30). Hugging Face says it doesn't store user data for training, but data handling past the router follows each provider's policy, with no per-request filter to exclude providers that train or retain (12 of 20). Request bodies and responses aren't stored when routed and debug logs are kept up to 30 days without user data or tokens, with provider retention outside that statement (10 of 15). Usage by model and provider in account settings, an Enterprise usage API, and Team and Enterprise admins can disable providers and set a spending limit (13 of 15). SOC 2 Type 2 for the Hub, of which the docs say Inference Providers is a feature, and security.txt valid to 2030 per the Inference Endpoints check; no bug bounty found (14 of 20).",
          "b": "Platform readings are the meta-model-api listing's of 4 November. Team keys, named, revocable, no scopes (20 of 30). Standard mode only, so content isn't used to train Meta models under the terms, with safety use reserved (17 of 20). Retention 'as needed' with zero retention through sales (10 of 15). Usage dashboard by model, no per-call log (5 of 15). No security.txt on dev.meta.ai, a bug bounty and a security commitments page, no named certification (12 of 20). 20 + 17 + 10 + 5 + 12 = 64."
        },
        "scores": {
          "a": 79,
          "b": 64
        }
      },
      {
        "by": 14,
        "category": "ergonomics",
        "leads": "muse-ember",
        "name": "Agent ergonomics",
        "reasons": {
          "a": "Router reading of the model checklist (tool use, structured output, caching, context, batch, SDKs, errors), as for openrouter. Tool calling through the OpenAI-compatible API with `tool_choice`, support flagged per provider in /v1/models (15 of 20). Structured output documented, support flagged per provider and false on some (10 of 15). No prompt caching guidance found, though the Enterprise usage API counts cached input tokens (3 of 15). Routes to models with up to 1,048,576 tokens of context per the model list, a judgement call since a router has no default model (15). No batch API (0 of 10). Official clients in Python and JavaScript, plus any OpenAI SDK (10). Errors not documented (5 of 15).",
          "b": "Model reading. Tool calls, parallel calls, computer use and search grounding, with tool_choice limited to auto per the platform reading (15 of 20). Strict structured output (15). Automatic prompt caching, cached input at 4% of the input price (15). A 1,048,576-token context, but requests over 200K input tokens bill at three times the rate for the whole request (12 of 15). No batch endpoint (0). No SDK of Meta's own; OpenAI and Anthropic SDKs against compatible endpoints (0). OpenAI-style errors with fixes and retryable codes (15). 15 + 15 + 15 + 12 + 0 + 0 + 15 = 72."
        },
        "scores": {
          "a": 58,
          "b": 72
        }
      }
    ],
    "facts": [
      {
        "a": "Model router",
        "b": "Model API",
        "name": "Kind"
      },
      {
        "a": "Hugging Face, Inc.",
        "b": "Meta",
        "name": "Vendor"
      },
      {
        "a": "https://router.huggingface.co/v1",
        "b": "https://api.meta.ai/v1",
        "name": "Hosted endpoint"
      },
      {
        "a": "HTTP",
        "b": "",
        "name": "Transports"
      },
      {
        "a": "API key",
        "b": "API key",
        "name": "Auth"
      },
      {
        "a": "Pay per use",
        "b": "Pay per use",
        "name": "Pricing"
      },
      {
        "a": "no",
        "b": "no",
        "name": "x402"
      },
      {
        "a": "Proprietary service under the Hugging Face Terms of Service, which name Inference Providers. The `huggingface_hub` Python client is Apache-2.0 and `@huggingface/inference` is MIT",
        "b": "Closed model, used through Meta Model API and Muse Code under the Meta Model API Terms of Service (last updated 9 October 2026). No open weights; Meta's open-weight models are Muse Spark Open and Muse Glimmer",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "yes",
        "b": "yes",
        "name": "llms.txt"
      },
      {
        "a": "2026-10-29",
        "b": "2026-11-09",
        "name": "Last release"
      },
      {
        "a": "2022-09-15",
        "b": "2026-10-02",
        "name": "Terms last updated"
      },
      {
        "a": "2023-03-28",
        "b": "couldn't be read",
        "name": "Privacy policy last updated"
      },
      {
        "a": "not found in the text",
        "b": "yes",
        "name": "Customer content may train models"
      },
      {
        "a": "not found in the text",
        "b": "not found in the text",
        "name": "Terms restrict automated access"
      },
      {
        "a": "not found in the text",
        "b": "yes",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "yes",
        "b": "not found in the text",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "not found in the text",
        "b": "yes",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "483k npm/wk, 71.5M PyPI/wk",
        "b": "none",
        "name": "Popularity"
      }
    ],
    "faq": [
      {
        "answer": "Hugging Face Inference Providers scores 54 (C) on agent readiness against Muse Ember's 51.8 (D), and leads in 4 of 7 scored categories. Muse Ember leads on reliability and agent ergonomics.",
        "question": "Which is better for AI agents, Hugging Face Inference Providers or Muse Ember?"
      },
      {
        "answer": "Both need an API key.",
        "question": "Do Hugging Face Inference Providers and Muse Ember need an API key?"
      },
      {
        "answer": "Yes. Hugging Face Inference Providers has a hosted endpoint at https://router.huggingface.co/v1 and Muse Ember at https://api.meta.ai/v1.",
        "question": "Can an agent call Hugging Face Inference Providers and Muse Ember without installing anything?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Security \u0026 auth, 79 against 64",
          "Maintenance \u0026 community, 80 against 46",
          "Transparency \u0026 trust, 71 against 53"
        ],
        "also": null,
        "goodFor": "Agents and developers who want open-weight models from several hosts on one token and one bill, with a public price and capability list per provider.",
        "slug": "hugging-face-inference-providers",
        "watchFor": "No rate limits, 429 behaviour or error responses documented for the router"
      },
      {
        "aheadOn": [
          "Reliability, 40 against 25",
          "Agent ergonomics, 72 against 58"
        ],
        "also": null,
        "goodFor": "Agents that hand out many small, bounded tasks (sub-agents, tool loops, routing, extraction over thousands of files) and want a cheap multimodal model with a 1M context behind the same API as a bigger Muse model.",
        "slug": "muse-ember",
        "watchFor": "No weights and no parameter count published, so it can't be run locally"
      }
    ],
    "history": null,
    "job": {
      "capability": "inference.llm",
      "name": "LLM inference"
    },
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        "edge": "muse-ember",
        "hugging-face-inference-providers": 58,
        "key": "ergonomics",
        "muse-ember": 72,
        "name": "Agent ergonomics",
        "weight": 13
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      {
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        "edge": "hugging-face-inference-providers",
        "hugging-face-inference-providers": 79,
        "key": "security",
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        "name": "Security \u0026 auth",
        "weight": 14
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        "edge": "",
        "hugging-face-inference-providers": 20,
        "key": "payments",
        "muse-ember": 20,
        "name": "Payments \u0026 pricing",
        "weight": 10
      },
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        "name": "Task success",
        "pending": true,
        "weight": 10
      },
      {
        "by": 34,
        "edge": "hugging-face-inference-providers",
        "hugging-face-inference-providers": 80,
        "key": "maintenance",
        "muse-ember": 46,
        "name": "Maintenance \u0026 community",
        "weight": 7
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        "edge": "hugging-face-inference-providers",
        "hugging-face-inference-providers": 71,
        "key": "transparency",
        "muse-ember": 53,
        "name": "Transparency \u0026 trust",
        "weight": 7
      }
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      "hugging-face-inference-providers": "One Hugging Face token reaches open models on 18 partner providers at the providers' own prices, with a public model list that shows each provider's price, context and tool support. Router rate limits, error responses and its own status component were not found in the reviewed documentation, and free accounts have had no included credits since 7 October 2026.",
      "muse-ember": "A cheap, capable model for the repeated parts of agent work, at $0.10 and $0.50 per million tokens with a 1M context and Meta's figures putting it level with Claude Haiku 5.5 on fewer tokens. Launched today on an API with no readable status page, SLA or deprecation policy, and closed, with no weights."
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  "markdown": "Hugging Face Inference Providers scores 54 (C) on agent readiness against Muse Ember's 51.8 (D), and leads in 4 of 7 scored categories. Muse Ember leads on reliability and agent ergonomics. Both do llm inference.\n\n- Hugging Face Inference Providers: grade C, 54/100, rank #928 of 1515. Markdown https://www.anchorterminal.com/tools/hugging-face-inference-providers.md · JSON https://www.anchorterminal.com/api/v1/tools/hugging-face-inference-providers.json\n- Muse Ember: grade D, 51.8/100, rank #1017 of 1515. Markdown https://www.anchorterminal.com/tools/muse-ember.md · JSON https://www.anchorterminal.com/api/v1/tools/muse-ember.json\n- Best model APIs and inference for AI agents: https://www.anchorterminal.com/best/inference/index.md\n- All 871 models comparisons: https://www.anchorterminal.com/compare/inference/index.md\n\n## Which one, for what\n\n### Hugging Face Inference Providers (C)\n\nGood for: Agents and developers who want open-weight models from several hosts on one token and one bill, with a public price and capability list per provider.\n\nAhead on:\n- Security \u0026 auth, 79 against 64\n- Maintenance \u0026 community, 80 against 46\n- Transparency \u0026 trust, 71 against 53\n\nWatch for: No rate limits, 429 behaviour or error responses documented for the router\n\n### Muse Ember (D)\n\nGood for: Agents that hand out many small, bounded tasks (sub-agents, tool loops, routing, extraction over thousands of files) and want a cheap multimodal model with a 1M context behind the same API as a bigger Muse model.\n\nAhead on:\n- Reliability, 40 against 25\n- Agent ergonomics, 72 against 58\n\nWatch for: No weights and no parameter count published, so it can't be run locally\n\n\n## Why the edge\n\n### Maintenance \u0026 community: Hugging Face Inference Providers +34\n\n- Hugging Face Inference Providers (80): Model reading. huggingface_hub 2.4.0 tagged on 29 October 2026 and the Inference Providers docs updated daily by an automated job, last on 6 November (30). huggingface_hub 2.2.0, 2.3.0 and 2.4.0 and @huggingface/inference 4.13.28 to 4.13.32 in the 90 days (20). No dedicated changelog; the Hub has a forum and Discord, and GitHub issue replies were not read (8 of 15). Current official SDKs in Python and JavaScript (15). The Python client supports Python 3.10 and later, per the Inference Endpoints check; CI not read (7 of 10).\n- Muse Ember (46): Model reading. Released 9 November 2026 (30). No deprecation policy or notice period found, per the platform reading (0 of 12). Dated launches on the API, the latest Muse Wonder on 12 October and Muse Ember on 9 November (8 of 8). No Model API changelog; a help centre with tickets (8 of 15). No SDK of Meta's own (0 of 15, 0 of 10). 30 + 0 + 8 + 8 + 0 = 46.\n\n### Transparency \u0026 trust: Hugging Face Inference Providers +18\n\n- Hugging Face Inference Providers (71): Closed service under Terms of Service that name Inference Providers, with an Apache-2.0 Python client and an MIT JavaScript client (20). The security page and the docs agree on no training, no stored bodies and 30-day logs, but the privacy policy dates from 28 March 2023 and each provider's own data policy governs the rest (18 of 30). No deprecation policy found. Provider support changes and the 7 October removal of free credits are dated only in the docs repository history (5 of 20). The privacy policy lists 11 subprocessors with countries per the Inference Endpoints check, and the docs name every provider a request can reach, though not where each runs (15 of 20).\n- Muse Ember (53): Editorial half. Closed model under the Model API terms, with clear terms and law; no weights and no parameter count, unlike Muse Spark Open and Muse Glimmer (15). Standard content isn't used for training, retention periods not stated outside zero retention (18 of 30). No deprecation policy or dated notices (0). A subprocessor list linked but not read, no data locations (8 of 20). 15 + 18 + 0 + 8 = 41.\n\n### Reliability: Muse Ember +15\n\n- Hugging Face Inference Providers (25): Hosted lines. status.huggingface.co is a Better Stack page with 90-day component history for the Hub, Inference Endpoints, Spaces, the CDNs and Jobs, but no component for Inference Providers or the router (10 of 20). With no component, there is no readable incident history for the surface agents call, and the feed's 23 items from 10 June to 7 October 2026 don't name it (5). No rate limits for the router were found; the Hub rate-limits page gives numbers for Hub APIs, resolvers and pages only (0). No 429 or overload guidance for the router was found (0). No SLA found (0). Chat completions on the router is generally available; the Responses API is labelled beta (10). Routing between providers with `:fastest` or `:cheapest` softens a single provider's outage, which this checklist doesn't score.\n- Muse Ember (40): Model reading, for the surface it's called on, Meta Model API. No status page with readable history, per the meta-model-api listing's 4 November check (0), so no readable incident history (5). Rate limits published per team and per model, 3,000 RPM and 4,000,000 TPM for muse-ember-1.0, read 9 November (15). 429 and 503 with Retry-After, backoff guidance and rate-limit headers, per the meta-model-api listing (15). No SLA found (0). Muse Ember carries no preview label, but the API opened as a public preview in July and no general-availability notice was found, so half, as for the platform (5). 0 + 5 + 15 + 15 + 0 + 5 = 40.\n\n### Security \u0026 auth: Hugging Face Inference Providers +15\n\n- Hugging Face Inference Providers (79): Model reading. Fine-grained user access tokens that can be limited to the `Make calls to Inference Providers` permission, rotated and revoked, plus a public revocation endpoint for leaked tokens, and organisation approval of fine-grained tokens on paid plans (30). Hugging Face says it doesn't store user data for training, but data handling past the router follows each provider's policy, with no per-request filter to exclude providers that train or retain (12 of 20). Request bodies and responses aren't stored when routed and debug logs are kept up to 30 days without user data or tokens, with provider retention outside that statement (10 of 15). Usage by model and provider in account settings, an Enterprise usage API, and Team and Enterprise admins can disable providers and set a spending limit (13 of 15). SOC 2 Type 2 for the Hub, of which the docs say Inference Providers is a feature, and security.txt valid to 2030 per the Inference Endpoints check; no bug bounty found (14 of 20).\n- Muse Ember (64): Platform readings are the meta-model-api listing's of 4 November. Team keys, named, revocable, no scopes (20 of 30). Standard mode only, so content isn't used to train Meta models under the terms, with safety use reserved (17 of 20). Retention 'as needed' with zero retention through sales (10 of 15). Usage dashboard by model, no per-call log (5 of 15). No security.txt on dev.meta.ai, a bug bounty and a security commitments page, no named certification (12 of 20). 20 + 17 + 10 + 5 + 12 = 64.\n\n### Agent ergonomics: Muse Ember +14\n\n- Hugging Face Inference Providers (58): Router reading of the model checklist (tool use, structured output, caching, context, batch, SDKs, errors), as for openrouter. Tool calling through the OpenAI-compatible API with `tool_choice`, support flagged per provider in /v1/models (15 of 20). Structured output documented, support flagged per provider and false on some (10 of 15). No prompt caching guidance found, though the Enterprise usage API counts cached input tokens (3 of 15). Routes to models with up to 1,048,576 tokens of context per the model list, a judgement call since a router has no default model (15). No batch API (0 of 10). Official clients in Python and JavaScript, plus any OpenAI SDK (10). Errors not documented (5 of 15).\n- Muse Ember (72): Model reading. Tool calls, parallel calls, computer use and search grounding, with tool_choice limited to auto per the platform reading (15 of 20). Strict structured output (15). Automatic prompt caching, cached input at 4% of the input price (15). A 1,048,576-token context, but requests over 200K input tokens bill at three times the rate for the whole request (12 of 15). No batch endpoint (0). No SDK of Meta's own; OpenAI and Anthropic SDKs against compatible endpoints (0). OpenAI-style errors with fixes and retryable codes (15). 15 + 15 + 15 + 12 + 0 + 0 + 15 = 72.\n\n## Score by category\n\n| Category | Weight | Hugging Face Inference Providers | Muse Ember | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 25 | 40 | Muse Ember +15 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 62 | 60 | Hugging Face Inference Providers +2 |\n| Agent ergonomics | 13% (16.2 this run) | 58 | 72 | Muse Ember +14 |\n| Security \u0026 auth | 14% (17.5 this run) | 79 | 64 | Hugging Face Inference Providers +15 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 20 | 20 | even |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 80 | 46 | Hugging Face Inference Providers +34 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 71 | 53 | Hugging Face Inference Providers +18 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **54 · C** | **51.8 · D** | |\n\n## Facts side by side\n\n| Fact | Hugging Face Inference Providers | Muse Ember |\n| --- | --- | --- |\n| Kind | Model router | Model API |\n| Vendor | Hugging Face, Inc. | Meta |\n| Hosted endpoint | `https://router.huggingface.co/v1` | `https://api.meta.ai/v1` |\n| Transports | HTTP |  |\n| Auth | API key | API key |\n| Pricing | Pay per use | Pay per use |\n| x402 | no | no |\n| Licence | Proprietary service under the Hugging Face Terms of Service, which name Inference Providers. The `huggingface_hub` Python client is Apache-2.0 and `@huggingface/inference` is MIT | Closed model, used through Meta Model API and Muse Code under the Meta Model API Terms of Service (last updated 9 October 2026). No open weights; Meta's open-weight models are Muse Spark Open and Muse Glimmer |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| Last release | 2026-10-29 | 2026-11-09 |\n| Terms last updated | 2022-09-15 | 2026-10-02 |\n| Privacy policy last updated | 2023-03-28 | couldn't be read |\n| Customer content may train models | not found in the text | yes |\n| Terms restrict automated access | not found in the text | not found in the text |\n| Terms restrict benchmarking | not found in the text | yes |\n| Terms or service can change without notice | yes | not found in the text |\n| Arbitration or class-action waiver | not found in the text | yes |\n| Popularity | 483k npm/wk, 71.5M PyPI/wk | none |\n\n## Verdicts\n\n**Hugging Face Inference Providers.** One Hugging Face token reaches open models on 18 partner providers at the providers' own prices, with a public model list that shows each provider's price, context and tool support. Router rate limits, error responses and its own status component were not found in the reviewed documentation, and free accounts have had no included credits since 7 October 2026.\n\n**Muse Ember.** A cheap, capable model for the repeated parts of agent work, at $0.10 and $0.50 per million tokens with a 1M context and Meta's figures putting it level with Claude Haiku 5.5 on fewer tokens. Launched today on an API with no readable status page, SLA or deprecation policy, and closed, with no weights.\n\n## Before you call either\n\n### Hugging Face Inference Providers\n\n1. Read https://router.huggingface.co/v1/models before choosing a provider; it shows price, context and `supports_tools` per provider\n2. Pin a provider with `model:\u003cprovider\u003e` when tool calling or structured output matters, since support varies by provider\n3. Use a fine-grained token with only the Inference Providers permission, and send `X-HF-Bill-To` to bill an organisation\n4. Back off on 429 without assuming a Retry-After header, since none is documented for the router\n\n### Muse Ember\n\n1. Call https://api.meta.ai/v1/responses (or /chat/completions or /messages) with model muse-ember-1.0 and a bearer MODEL_API_KEY\n2. Set the effort explicitly, reasoning.effort on Responses or reasoning_effort on Chat Completions. It defaults to max, and none isn't accepted; minimal or low suits classification and routing\n3. Keep input under 200K tokens a request, cached tokens and previous_response_id history included, or the whole request bills at the long-context rate\n4. Use it for sub-agents and bounded repeated tasks, and a larger Muse model for the plan, as Meta advises\n5. Use strict structured output for extraction and routing steps\n6. In Claude Code, map the haiku alias and sub-agent model to muse-ember-1.0, per Meta's guide\n\n## Questions\n\n### Which is better for AI agents, Hugging Face Inference Providers or Muse Ember?\n\nHugging Face Inference Providers scores 54 (C) on agent readiness against Muse Ember's 51.8 (D), and leads in 4 of 7 scored categories. Muse Ember leads on reliability and agent ergonomics.\n\n### Do Hugging Face Inference Providers and Muse Ember need an API key?\n\nBoth need an API key.\n\n### Can an agent call Hugging Face Inference Providers and Muse Ember without installing anything?\n\nYes. Hugging Face Inference Providers has a hosted endpoint at https://router.huggingface.co/v1 and Muse Ember at https://api.meta.ai/v1.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-muse-ember.json, and with the fewest tokens: https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-muse-ember.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"hugging-face-inference-providers\", \"b\": \"muse-ember\"}`. From a terminal: `anchor compare hugging-face-inference-providers muse-ember`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/hugging-face-inference-providers.json and https://www.anchorterminal.com/api/v1/tools/muse-ember.json\n\n## Other comparisons with Hugging Face Inference Providers or Muse Ember\n\n- [Amazon Bedrock vs Hugging Face Inference Providers](https://www.anchorterminal.com/compare/amazon-bedrock-vs-hugging-face-inference-providers.md)\n- [Amazon Bedrock vs Muse Ember](https://www.anchorterminal.com/compare/amazon-bedrock-vs-muse-ember.md)\n- [Claude API vs Hugging Face Inference Providers](https://www.anchorterminal.com/compare/anthropic-api-vs-hugging-face-inference-providers.md)\n- [Claude API vs Muse Ember](https://www.anchorterminal.com/compare/anthropic-api-vs-muse-ember.md)\n- [Antseed vs Muse Ember](https://www.anchorterminal.com/compare/antseed-vs-muse-ember.md)\n- [Azure OpenAI in Foundry Models vs Hugging Face Inference Providers](https://www.anchorterminal.com/compare/azure-openai-vs-hugging-face-inference-providers.md)\n- [Azure OpenAI in Foundry Models vs Muse Ember](https://www.anchorterminal.com/compare/azure-openai-vs-muse-ember.md)\n- [BlockRun.AI vs Hugging Face Inference Providers](https://www.anchorterminal.com/compare/blockrun-ai-vs-hugging-face-inference-providers.md)\n- [BlockRun.AI vs Muse Ember](https://www.anchorterminal.com/compare/blockrun-ai-vs-muse-ember.md)\n- [Cerebras Inference vs Hugging Face Inference Providers](https://www.anchorterminal.com/compare/cerebras-inference-vs-hugging-face-inference-providers.md)\n- [Cerebras Inference vs Muse Ember](https://www.anchorterminal.com/compare/cerebras-inference-vs-muse-ember.md)\n- [Cloudflare AI Gateway vs Hugging Face Inference Providers](https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-hugging-face-inference-providers.md)\n- [Cloudflare AI Gateway vs Muse 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API](https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-mistral-api.md)\n- [Hugging Face Inference Providers vs Muse Spark Open](https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-muse-spark-open.md)\n- [Hugging Face Inference Providers vs Novita AI](https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-novita-ai.md)\n- [Hugging Face Inference Providers vs OpenAI API](https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-openai-api.md)\n- [Hugging Face Inference Providers vs Beam 1.0](https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-reflection-beam.md)\n- [Hugging Face Inference Providers vs SambaCloud](https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-sambanova.md)\n- [Hugging Face Inference Providers vs SiliconFlow](https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-siliconflow.md)\n- [Hugging Face Inference Providers vs 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Ember](https://www.anchorterminal.com/compare/jais-3-vs-muse-ember.md)\n- [Meta Model API vs Muse Ember](https://www.anchorterminal.com/compare/meta-model-api-vs-muse-ember.md)\n- [MiniMax API vs Muse Ember](https://www.anchorterminal.com/compare/minimax-api-vs-muse-ember.md)\n- [Mistral AI API vs Muse Ember](https://www.anchorterminal.com/compare/mistral-api-vs-muse-ember.md)\n- [Muse Ember vs Muse Spark Open](https://www.anchorterminal.com/compare/muse-ember-vs-muse-spark-open.md)\n- [Muse Ember vs Nemotron 3.5 Super VL](https://www.anchorterminal.com/compare/muse-ember-vs-nemotron-3-5-super-vl.md)\n- [Muse Ember vs Novita AI](https://www.anchorterminal.com/compare/muse-ember-vs-novita-ai.md)\n- [Muse Ember vs OpenAI API](https://www.anchorterminal.com/compare/muse-ember-vs-openai-api.md)\n- [Muse Ember vs OpenRouter](https://www.anchorterminal.com/compare/muse-ember-vs-openrouter.md)\n- [Muse Ember vs Prism 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    "title": "Hugging Face Inference Providers vs Muse Ember for AI agents (2026)",
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